Analyzing clinical trial outcomes based on incomplete daily diary reports.
Analyzing clinical trial outcomes based on incomplete daily diary reports.
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根据不完整的每日日记报告分析临床试验结果。
DOI：
10.1002/sim.6890
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发表时间：
2016
影响因子：
2
通讯作者：
Little,RoderickJA
中科院分区：
文献类型：
--
作者：
Thomas,Neal;Harel,Ofer;Little,RoderickJA
本文介绍了一项病例研究，评估了缺失数据对一项评估药物治疗失眠效果的研究中每日日记数据分析的影响。主要分析将每例患者的每日日记值平均为每周变量。按照常用方法，如果日记报告的数量最少（即，至少4）。然后将纵向模型与治疗、时间和患者特异性效应拟合。从模型中获得预先规定的标志时间的治疗效果。脱落后的每周值被视为缺失，但间歇性每日缺失值被掩盖。提供了图形总结和表格，以表征复杂缺失数据模式。我们对每日日记数据使用多重插补来创建完整的数据集，以便每周患者平均值正好有7个每日日记值。然后应用标准分析方法对完整数据集进行地标分析，并使用标准多重插补方法合并所得估计值。观察到的数据受到数字堆积和模式化响应（例如，连续几天的相同值），这使得响应数据的准确建模变得困难。在不同建模假设下对数据进行敏感性分析，沿着模式混合模型评估对随机缺失假设的敏感性。重点是可以用通用软件实现的图形显示和计算方法。版权所有© 2016约翰威利父子有限公司.
A case study is presented assessing the impact of missing data on the analysis of daily diary data from a study evaluating the effect of a drug for the treatment of insomnia. The primary analysis averaged daily diary values for each patient into a weekly variable. Following the commonly used approach, missing daily values within a week were ignored provided there was a minimum number of diary reports (i.e., at least 4). A longitudinal model was then fit with treatment, time, and patient‐specific effects. A treatment effect at a pre‐specified landmark time was obtained from the model. Weekly values following dropout were regarded as missing, but intermittent daily missing values were obscured. Graphical summaries and tables are presented to characterize the complex missing data patterns. We use multiple imputation for daily diary data to create completed data sets so that exactly 7 daily diary values contribute to each weekly patient average. Standard analysis methods are then applied for landmark analysis of the completed data sets, and the resulting estimates are combined using the standard multiple imputation approach. The observed data are subject to digit heaping and patterned responses (e.g., identical values for several consecutive days), which makes accurate modeling of the response data difficult. Sensitivity analyses under different modeling assumptions for the data were performed, along with pattern mixture models assessing the sensitivity to the missing at random assumption. The emphasis is on graphical displays and computational methods that can be implemented with general‐purpose software. Copyright © 2016 John Wiley & Sons, Ltd.